May 2014 

NASA/CR–2014-218274 

Myanmar Ecological Forecasting: Utilizing 

NASA Earth Observations to Monitor, Map, and 

Analyze Mangrove Forests in Myanmar for 

Enhanced Conservation 

Samuel J. Weber, Louis Keddell, and Mohammed Kemal 

Science Systems and Applications, Inc., Greenbelt, Maryland 
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May 2014 

NASA/CR–2014-218274 

Myanmar Ecological Forecasting: Utilizing 

NASA Earth Observations to Monitor, Map, and 

Analyze Mangrove Forests in Myanmar for 

Enhanced Conservation 

Samuel J. Weber, Louis Keddell, and Mohammed Kemal 

Science Systems and Applications, Inc., Greenbelt, Maryland 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 
2 

Abstract 

Mangroves supply many essential environmental amenities, such as preventing soil 

erosion, filtering water pollution, and protecting shorelines from harmful waves, floods, 

storms and winds. The Mangroves in Myanmar not only provide citizens with a food 

source, but they also offer firewood, charcoal, and construction materials. The 

depletion of mangroves is threatening more than the biodiversity however; Myanmar’s 

fiscal livelihood is also in harm’s way. Mangroves are valued at $100,000 to $277,000 per 

square kilometer and if managed in a sustainable fashion, can infuse constant income 

to the emerging Myanmarese economy. This study analyzed three coastline regions, 

the Ayeyarwady Delta, Rakhine and Tanintharyi, and mapped the spatial extent of 

mangrove forest during the dry season in 2000 and 2013. 

The classifications were derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) 

and Landsat 8 Operation Land Imager (OLI) imagery, as well as the Terra Advanced 

Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation 

model information. This data was atmospherically corrected, mosaicked, masked and 

classified in ENVI, followed by ArcGIS to perform raster calculations and create final 

products. Forest degradation collected from 2000 to 2013 was later used to forecast the 

density and health of Mangroves in the year 2030. These results were subsequently 

presented to project partners Dr. Peter Leimgruber and Ellen Aiken at the Smithsonian 

Conservation Biology Institute in Front Royal, VA. After the presentation of the project to 

the partners, these organizations formally passed on to the Myanmar Ministry of 

Environment, Conservation and Forestry for policy makers and forest managers to utilize 

in order to protect the Myanmar mangrove ecosystem while sustaining a healthy 

economy. 
3 

Introduction 

Mangrove forests are one of the most valuable, thriving, and diverse ecosystems on the 

planet, but they are becoming increasingly exploited and mismanaged (Lee 1999; Giri 

et al. 2008). In Myanmar, many of these mangrove stands flourished for centuries, 

virtually untouched until extensive deforestation began in the late 1970’s. At the time 

there was no legislation to promote sustainable forest management, and as a result the 

forests were depleted at alarming rates. During the 1990’s, multiple environmental acts 

were passed to help regulate tree harvesting processes, including the 1992 Forest Law 

and the 1995 Forest Policy, but they were only mildly successful and difficult to enforce 

(Oo 2002). The 2000’s brought a new chapter as Myanmar’s expanding economy and

lifted political embargos created an explosion of infrastructure and agriculture, once 

again encroaching on the mangrove’s habitat. Even with the increasing development 

and encroachment on mangroves, Myanmar’s long state of isolation has made it one 

of the most species rich countries in all of South-east Asia, and is considered to be one 

of the last strongholds for large mammals such as tigers and elephants (Leimgruber et 

al. 2005). In a country with such rich biodiversity and a large dependence on natural 

resources for income, fuel, and food, preserving Myanmar’s mangroves and raising 

awareness about sustainability has become a national priority. 

This study mapped the spatial extent of three main mangrove regions along the coast 

of Myanmar during 2000 and 2013, including the Ayeyarwardy Delta, Rakhine and 

Tanintharyi regions (Oo 2002). The three regions are spread along the coast and vary in 

population density, which provides a valuable comparison among the regions as to 

how human and economic pressures can affect mangroves. The Ayeyarwady Delta is 

centrally located and has the highest population density, followed by Rakhine to the 

north, and the most remote region being the Tanintharyi to the south. A land change 

model was then used to produce change maps between 2000 and 2013 and project 

mangrove coverage to the year 2030 to help resource managers and policy makers 

craft future decisions. Once the mangroves were classified and projected, SRTM data 

were used to derive tree canopy heights and biomass estimations using allometric 

equations. 

Mangroves in Myanmar house thriving biodiversity and provide citizens with essential 

natural products such as food, firewood, and construction materials (Oo 2002). This 

ecological forecasting project helped the Myanmar government visualize and quantify 

their current largest mangrove areas, as well as shed light on the success of previous 

preservation efforts that may influence future conservation strategies. This project 

ultimately allowed important decision makers to assess the negative impacts that have 

occurred due to the deforestation and degradation of mangrove ecosystems. To 

successfully implement this study and its findings, Dr. Peter Leimgruber and Ellen Aiken 

at the Smithsonian Conservation Biology Institute were irreplaceable as they officially 

handed off the project and its decision making tools to the Myanmar Ministry of 

Environment, Conservation and Forestry. The project will serve as a valuable reference 

for efficiently allocating resources and man power, while adapting new management 

strategies to the changing mangrove landscape. 
4 

Methodology 

Data Acquisition: 

A total of 12 scenes from Landsat 7 and 8 encompassing these study areas were 

downloaded from the U.S. Geological Survey Global Visualization Viewer (GloVIS) 

website. Three scenes encompassed the Rakhine region (path 134/row 46, path 

135/row 46, path 134/row 47), two scenes for the Tanintharyi region (path 130/row 51, 

path 130/row 52), and one scene for the Ayeyarwady Delta region (path 133/ row 49). 

Six Landsat 7 scenes were downloaded within a November through May time period 

during Myanmar’s dry season for 2000 and six Landsat 8 scenes were downloaded for 

this same six-month period for 2013 in order to reduce phenological differences that 

may interfere with the image classification (Kovacs, Wang, and Blanco-Correa 2001; 

Oo 2002). The imagery acquired was also selected based on lowest cloud cover and 

visibility, which can prove difficult in tropical regions where cloud cover is high (Sano et 

al. 2007). The Landsat 7 and 8 imagery were all Level 1 terrain-corrected (L1T) products, 

which have been radiometrically and geometrically corrected through the inclusion of 

ground control points and digital elevation model data for topographic accuracy 

(NASA 2011). Landsat has visible, near infrared, and shortwave infrared bands that are 

suited to distinguishing between the spectral signatures of different land cover types, 

and will be used for the calculation of NDVI (Normalized Difference Vegetation Index), 

and will assist in the mangrove classifications and change detections for 2000 – 2013. 

In addition to the Landsat imagery, ASTER digital elevation model data, Shuttle Radar 

Topography Mission (SRTM) digital elevation data, and SRTM Waterbody Data were 

downloaded from the Earth Explorer website for these locations. The SRTM Data were 

derived from the joint 11-day mission operated by NGA and NASA aboard the Shuttle 

Endeavour (Ramirez 2009). The 30m ASTER DEMs were generated from the ASTER sensor 

operated by NASA and Japan’s Ministry of Economic Trade and Industry (METI) 

onboard the Terra satellite (Tan 2012). 

Processing: 

The Landsat imagery was first atmospherically corrected using the QUAC (QUick 

Atmospheric Correction) tool in ENVI (Exelis 2013). The individual bands (excluding the 

thermal, coastal aerosol, and cirrus bands) were then stacked and an NDVI was 

created from atmospherically corrected reflectance values and added to the layer 

stack as well. The ASTER DEMs were then loaded into ENVI and mosaicked together. 

Two masks were then generated; one mask based on elevation, and one based on the 

SRTM Waterbody Data shapefile. The elevation mask was set to remove all areas in the 

Landsat image greater than 35 meters, and the SRTM water boundary data masked out 

all coastal waters. The elevation mask threshold was set to 35 meters, as mangroves 

are not expected to grow above this mark (Fatoyinbo and Simard 2013).Through these 

two masks, the areas of the image to be classified were reduced in order to limit 

misclassification and gain greater focus and accuracy on the mangroves themselves. 

Once the Landsat tiles were masked, the remaining portion of the images were 

classified using an Iterative Self-Organizing Data Analysis Technique (ISODATA) 

unsupervised classification with parameters set to 7 iterations, minimum 40 classes (100+ 
5 

classes for Ayeyarwady and Rakhine due to difficulty of separating spectrally similar 

classes) , and a convergence threshold of 2.5%. The classes were then grouped and 

sieved with a group minimum threshold of 65. 

In addition to the classification maps for each of the three regions, biomass maps were 

produced using canopy heights derived from the SRTM DEM data and mangrove 

height and biomass equations. A global stand height-biomass allometric equation has 

been calculated for mangroves and was used to produce the biomass statistics for this 

project (Fatoyinbo and Simard 2013; Fatoyinbo et al. 2008; Saenger and Snedaker 

1993): 

Height (m) = 1.12 x Hsrtm – 2.19 

Biomass = 10.8 x H (m) + 34.9 

Change maps were also produced using a change detection map tool from 2000 –

2013 using Idrisi. These change maps were then input into the land cover change 

model in Idrisi in order to project mangrove cover for 2030. 

Data Analysis: 

Mangrove area and extent were quantified from the classification, change, and 

projection maps for the 2000 - 2030 period. In addition to quantifying the area and 

extent of mangroves, these maps demonstrated the changes occurring for mangroves 

within each region and areas most at risk in the future. The height and biomass data 

were visualized through a histogram in order to better represent the distribution of 

different mangroves within and among the three regions. 

A validation was also performed for the mangrove extent classification maps using the 

accuracy assessment tools in ENVI Classic. Error matrices were produced based on the 

accuracy of the classification compared to the ground truth regions of interest digitized 

in ENVI. Accuracies ranging from 46% to 84% were recorded among the three regions 

based on this initial validation effort. Due to the lack of time, this project was unable to 

further pursue improvements in map accuracy for the Ayeyarwady Delta region, which 

had the lowest accuracy reported. Based on the error matrices and visual assessment 

of the maps, sources of error and uncertainty were difficult to identify in some locations 

due to the lack of high resolution imagery, especially when going back in time towards 

2000. Perhaps future studies will be able to further delve into specific regions in 

Myanmar’s coastal regions that contain more high resolution imagery available through 

Google Earth, which can also be used as a validation exercise through the generation 

of random points (Potere et al. 2009; Tateishi et al. 2011). 
6 

Results 

2000 – 2013 Change 

Using the unsupervised ISODATA classification, mangrove extent maps were generated 

for 2000 and 2013 for the three main regions of interest (figure1). The Tanintharyi region 

had the most mangroves in 2000 with 2075km2, followed by Rakhine with 1734km2, and 

the Ayeyarwady Delta with 818km2. By 2013, a total of 655km2 The Ayeyarwady Delta 

saw the largest loss of mangroves at 356km2, followed by Rakhine at 264km2 and 

Tanintharyi at 35km2. The Ayeyarwady Delta saw widespread loss throughout the delta, 

the Rakhine saw large segments of deforestation centrally located within the coastal 

region, while the Tanintharyi saw a few scattered patches of disturbance along the 

coast. In addition to massive mangrove areas being deforested, biomass production 

among the remaining trees is also decreasing. Biomass is an important proxy for overall 

ecosystem health and total carbon storage. If biomass drops, so does the mangrove’s 

ability to absorb gaseous carbon and wildlife habitats will be lost. 

 2000 Extent 2013 Extent 

 Tanintharyi Region 

 

 

 Rakhine Region
7 

 

Ayeyarwardy Delta Region 

Figure 1: Aside from the 

Ayeyarwardy region, mangrove 

extent change is difficult to see 

from such a distance, hinting 

that small scale deforestation

and afforestation are very 

common
8 

2000-2013 Change 

Figure 2: Magnified areas shown in the inset boxes 

highlight deforestation and afforestation from 2000 to 

2013. Tanintharyi saw minimal change, Rakhine 

experienced modest change, and the Ayeyarwardy 

Delta was drastically modified. (Note: The 

Ayeyarwady map is at 1/3 the scale of the other two)
9 

Projected mangrove change 

The Idrisi Land Change Modeler produced two future map projections for 2030 based 

on mangrove change inputs from 2000 – 2013, ASTER elevation data, and a population 

map. Between the three regions, a total mangrove area of 1376km2 was lost, nearly a 

twofold increase from the deforestation extent from 2000 – 2013. The Rakhine region 

lost the most mangroves at 782km2, followed by the Ayeyarwady Delta at 332km2, and 

the Tanintharyi at 262km2. The “soft” prediction map, which produced a scale of 

mangrove vulnerability, illustrated the widespread risk to mangroves within the three 

regions (figure3). Much of the medium to high risk areas appeared to lie in the more 

fragmented stands, particularly evident for the Ayeyarwady Delta and Rakhine regions. 

The “hard” prediction maps for 2030 showed a slightly different picture, with the 

Ayeyarwady Delta and Rakhine regions being particularly hard hit while the Tanintharyi 

region remained largely untouched (figure4). As illustrated by the higher risk areas in 

the vulnerability maps for the Ayeyarwady Delta and Rakhine regions, the smaller 

fragmented mangrove stands are largely gone by 2030 in the hard prediction results. 

Projected Vulnerability 2013-2030 
10

2013-2030 Change 

Figure 3: Vulnerability represents an area’s potential to 

change, and helps highlight mangrove regions most at 

risk for future deforestation. Mangrove stands in orange 

and red are the most likely to vanish by 2030.
11

Rakhine Km2

2000 1734

2013 1470

Projected 

2030 688

Ayeyarwardy Km2

2000 818

2013 462

Projected 

2030 130

Tanintharyi Km2

2000 2075

2013 2040

Projected 

2030 1778

Figure 4: The Rakhine and Ayeyarwardy regions are 

forecasted to undergo disastrous deforestation from 2013-

2030. Many of the smaller fragmented stands will not survive 

and this loss will put heavy stress on both local communities

and the environment if not managed sustainably.

Figure 5: With total mangrove areas and biomass production 

rapidly declining over time, carbon capacity, biodiversity, natural 

resource production, and lucrative export availability will also drop.
12

Discussion 

2000 – 2013 Change 

Though the Ayeyarwady Delta probably had a much larger proportion of mangroves 

originally, pre-2000 deforestation rates coupled with the high population density 

resulted in this region having the lowest mangrove area of the three. The Tanintharyi 

region has a lower population density, which may explain the healthy state of 

mangroves in that remote region compared to the Ayeyarwady Delta and Rakhine 

regions (Oo 2002). Signs of pre-2000 mangrove disturbance were visible within the 2000 

Landsat 7 imagery, indicating the pressures already faced by the mangroves due to 

increasing economic development as Myanmar enacted political reforms and left its 

previous isolated state. 

The losses seen since 2000 were largely due to agricultural expansion and large scale 

deforestation (Giri et al. 2008). There was some evidence of mangrove clearing for 

aquaculture, but this was minor compared to the other two causes of disturbance. As 

seen in the change maps, there was also some afforestation, which may be evidence 

of efforts by Myanmar’s government and collaborative efforts with NGOs to replant 

mangrove forests and protect existing stands. Myanmar has acknowledged the 

potential benefit of coastal mangroves, and has previously enacted some legislation 

and set up mangrove plantations to attempt regrowth efforts (Oo 2002). The 

Ayeyarwady Delta saw massive deforestation during this 13 year period, but the two 

main mangrove stands to the south appear largely unharmed, a reflection of their 

status as protected areas, including the Mein-ma-hla Kyun Wildlife Sanctuary. The 

Rakhine region also saw major deforestation in a few central areas, while the 

Tanintharyi was largely untouched, most likely due to the remote nature of the region. 

2013 – 2030 Change 

The huge area of loss projected in the future for Myanmar’s mangroves indicates a 

need to address current methods of natural resource management and enforcement. 

While the total loss of mangrove area was much greater by 2030 than from 2000 – 2013, 

there was hope for some of Myanmar’s mangroves in the Tanintharyi region, where the 

projected change map showed very little loss in 2030 compared to the other two 

regions. However, this may be a result of continued low population density within the 

Tanintharyi, reflecting issues of accessibility and proximity rather than better 

conservation practices. A total of 2031km2 was lost between the three regions, roughly 

equivalent to the area of Maryland’s Prince George’s County, Howard County, and 

half of Washington, DC’s area combined. Without a change in current practices and 

laws, mangroves are projected to be largely non-existent in the Ayeyarwady Delta and 

Rakhine regions, where large segments of Myanmar’s population will be vulnerable to 

natural disasters and negative impacts on the local economy, side-effects of a major 

loss in mangroves. 

Errors, Uncertainty, and Future Work 

There were several sources of uncertainty and error in this project. When conducting 

unsupervised classification, there will always be certain pixels that prove difficult to 

assign to the proper class due to mixed land cover present within the pixel or other 

artifacts that may interfere with the classification. The lack of ground truth and 
13

unfamiliarity with the different forms of mangrove species within the region also 

hampered the classification process, as mangroves in different stages of growth and 

degradation took different appearances in different regions. Although the 35m mask 

was able to narrow the classification area, there was still other vegetation present that 

could be hard to distinguish from mangrove cover, and could pose a problem in 

border regions when the mangroves would mix with other vegetative cover. The 

validation effort sought to quantify the error found within each of the three classified 

regions, but there were limitations in the lack of high resolution imagery available in 

Myanmar for the years of interest. 

The Land Change Modeler had the potential to contain much uncertainty as is 

expected with any model. Thus we decided to follow the lead of the Myanmar 

ecological forecasting team from the summer of 2013 to not forecast our predictions 

beyond 2030, where uncertainty would be sure to increase drastically. Although there 

is no way to validate the future projection, we did get an indication of the confidence 

of the projection maps based on the amount of disturbance seen within currently 

protected mangrove areas. Though there was some deforestation within the Mein-mahla Kyun Wildlife Sanctuary in the Ayeyarwady Delta, it was still relatively well-off 

compared to the surrounding mangrove stands, increasing our confidence in the 

predictive ability of the model. 

There is much that can still be added to this project in the interest of Myanmar’s 

mangroves. Due to time constraints and logistical hurdles, we were not able to 

completely map the coast of Myanmar in its entirety. Providing extent, change and 

projection maps for the coast of Myanmar would be valuable to our project end users 

and partners, and would allow comparison with mangrove data previously measured 

by other studies, including the global mangrove maps produced for the year 2000 by 

Chandra Giri’s team at the USGS (Giri et al. 2011). 

We also did not have detailed ancillary data to make any conclusions based on forest 

management, fuelwood use, agriculture, fishing rates, or aquaculture. Future 

endeavors could provide a more comprehensive picture of the state of Myanmar’s 

mangroves, including extent maps going further back in time, assessing the damage 

and recovery before and after natural disasters such as the 2004 tsunami and cyclone 

Nargis in 2008, the construction of the port in Dawei, and replicating our methods using 

open source software in order to increase accessibility and reduce costs for our end 

users. 

Conclusions 

Mangroves are a valuable natural resource that offer a multitude of environmental and 

economic benefits. They facilitate pollution filtration, protection from storms, waves, 

and wind, and are home to a diverse range of species. Economically, mangroves are 

valued at $100,000 to $277,000 per square kilometer and provide many tangible 

benefits in the form of food, firewood, and construction materials (Green et al. 1998). 
14

Through the classification and change maps produced for 2000 – 2013, this project has 

demonstrated the major loss in mangrove extent that has occurred within the three 

largest mangrove regions of Myanmar. Our future projections for mangrove extent to 

2030 paint an even grimmer picture for Myanmar’s mangroves, with an even greater 

area forecast to be lost. From 2000 to 2030, an area equivalent to the size of Prince 

George’s county, MD; Howard county, MD; and half of Washington, DC was forecast to 

be lost. The scale of this loss is having a profound influence on Myanmar’s economy 

and the livelihoods of Myanmar’s citizens. With projections indicating mangrove health 

and extent only to get worse, Myanmar’s economy will only suffer further as the 

abundance of local aquatic species decreases, environmental quality degrades, and 

as the risk of storm damage increases without the protective coastal barrier provided 

by mangroves. 

Myanmar’s government has acknowledged the importance of mangroves and has 

facilitated some efforts to replant mangroves through plantations and local efforts, but 

as shown by the change maps, the rate of deforestation and degradation is far 

surpassing the regrowth effort (Oo 2002). Unless Myanmar introduces more effective 

mangrove legislation, protection, and enforcement practices, its coastal mangroves will 

only continue to decrease as the country increasingly opens itself up to economic 

development and the pressures of global markets. New natural resource 

management strategies will need to be developed to adapt to the changing nature of 

Myanmar’s mangroves. 

The satellite remote sensing methods used to produce the maps for this project offer a 

rapid, low cost solution for monitoring Myanmar’s mangroves. Traditional methods 

would require much time, effort and money in the form of field surveys by plane or trips 

into the field, which may not be feasible over such a large area and in remote regions 

of Myanmar (Blasco, Aizpuru, and Gers 2001). With free Landsat imagery archives 

spanning over 40 years with a spatial resolution ideal for capturing mangrove forest 

disturbance, and elevation data available online in the form of SRTM and ASTER 

imagery, rapid, large scale mapping and monitoring efforts can be conducted 

(Goward et al. 2006). With the ability to update maps in a timely manner, Myanmar will 

be better suited to inform its policy makers to the conditions of coastal mangroves and 

will be in a better position to focus its rehabilitation and conservation efforts. We hope 

that the tools and methods produced by this project will allow Myanmar to recover 

quickly in the interest of its economy and people. 

Acknowledgments 

We would like to thank our project partners Dr. Peter Leimgruber and Ellen Aiken at the 

Smithsonian Conservation Biology Institute for their advice and collaborative efforts. 

Our project also benefited greatly from the advice and mentorship of our science 

advisors, Fritz Policelli and Dr. Temilola Fatoyinbo of NASA GSFC, and we would like to 

thank them for their time. We would also like to thank the summer 2013 Myanmar 

ecological forecasting team for taking the time to introduce us to the Idrisi Land 

Change Modeler. And lastly, we would like to thank the NASA DEVELOP National 
15

Program Office for providing the logistics and support needed to keep our project 

running smoothly, even during a government shutdown. 

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REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE 

Contractor Report

 4. TITLE AND SUBTITLE

Myanmar Ecological Forecasting: Utilizing NASA Earth Observations to 

Monitor, Map, and Analyze Mangrove Forests in Myanmar for Enhanced 

Conservation 

5a. CONTRACT NUMBER

NNL11AA00B

 6. AUTHOR(S)

Weber, Samuel J.; Keddell, Louis; Kemal, Mohammed

 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

NASA Langley Research Center 

Hampton, Virginia 23681 

 

 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

 8. PERFORMING ORGANIZATION

 REPORT NUMBER

10. SPONSOR/MONITOR'S ACRONYM(S)

NASA

13. SUPPLEMENTARY NOTES

Work was performed by Science Systems and Applications, Inc., for STARSS II and Wise County under NASA Contract 

NNL11AA00B

Langley Technical Monitor: Michael L. Ruiz

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category 43

Availability: NASA CASI (443) 757-5802

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email: help@sti.nasa.gov)

14. ABSTRACT

Mangroves supply many essential environmental amenities, such as preventing soil erosion, filtering water pollution, and protecting shorelines from harmful 

waves, floods, storms and winds. The Mangroves in Myanmar not only provide citizens with a food source, but they also offer firewood, charcoal, and 

construction materials. The depletion of mangroves is threatening more than the biodiversity however; Myanmar’s fiscal livelihood is also in harm’s way. 

Mangroves are valued at $100,000 to $277,000 per square kilometer and if managed in a sustainable fashion, can infuse constant income to the emerging 

Myanmarese economy. This study analyzed three coastline regions, the Ayeyarwady Delta, Rakhine and Tanintharyi, and mapped the spatial extent of 

mangrove forest during the dry season in 2000 and 2013. The classifications were derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and 

Landsat 8 Operation Land Imager (OLI) imagery, as well as the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital 

elevation model information. This data was atmospherically corrected, mosaicked, masked and classified in ENVI, followed by ArcGIS to perform raster 

calculations and create final products. Forest degradation collected from 2000 to 2013 was later used to forecast the density and health of Mangroves in the 

year 2030. These results were subsequently presented to project partners Dr. Peter Leimgruber and Ellen Aiken at the Smithsonian Conservation Biology 

Institute in Front Royal, VA. 

15. SUBJECT TERMS

Biomass; Canopy height; Deforestation; Mangroves; Myanmar; Remote sensing

18. NUMBER

 OF 

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20

19b. TELEPHONE NUMBER (Include area code)

(443) 757-5802

a. REPORT

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c. THIS PAGE

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b. ABSTRACT

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17. LIMITATION OF 

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Prescribed by ANSI Std. Z39.18

Standard Form 298 (Rev. 8-98)

3. DATES COVERED (From - To)

November 2013

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

 389018.02.15.01.47 

11. SPONSOR/MONITOR'S REPORT

 NUMBER(S)

NASA/CR-2014-218274

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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

1. REPORT DATE (DD-MM-YYYY)

01- 05 - 2014